Design for Additive Manufacturing: Internal Channel Optimization

Abstract

The new possibilities offered by additive manufacturing (AM) can be exploited in gas turbines to produce a new generation of complex and efficient internal coolant systems. The flexibility offered by this new manufacturing method needs a paradigm shift in the design approach, and a possible solution is offered by topology optimization. The overall goal of this work is to propose an innovative method to design internal channels in gas turbines that fully exploit AM capabilities. The present work contains a new application of a fluid topology sedimentation method to optimize the internal coolant geometries with minimal pressure losses while maximizing the heat exchange. The domain is considered as a porous medium with variable porosity: the solution is represented by the final solid distribution that constitutes the optimized structure. In this work, the governing equations for an incompressible flow in a porous medium are considered together with a conjugate heat transfer equation that includes porosity-dependent thermal diffusivity. An adjoint optimization approach with steepest descent method is used to build the optimization algorithm. The simulations are carried out on three different geometries: a U-bend, a straight duct, and a rectangular box. For the U-bend, a series of splitter is automatically generated by the code, minimizing the stagnation pressure losses. In the straight duct and in the rectangular box, the impact of different choices of the weights and of the definition of the porosity-dependent thermal diffusivity is analyzed. The results show the formation of splitters and bifurcations in the box and “riblike” structures in the straight duct, which enhance the heat transfer.

Three-dimensional fluid topology optimization for heat transfer

“…This constant can be chosen in order to tune the speed of convergence. Furthermore, the relation (24) should be adapted in order to avoid non physical negative impermeability and to avoid the impermeability term α max γ to exceed the threshold value α max that is Validations of the code have been carried out (Pietropaoli et al 2017) comparing the results with experimental data by Von Karman Institute (Verstraete et al 2013).…”

“…The domain is not uniformly heated, but heat is directly diffused from the domain walls (Marck et al 2013). A similar problem is tackled in Pietropaoli et al in 2016 by using a steepest descent gradient method for the optimization process (Pietropaoli et al 2017).…”

“…A description of the problem is present in the work together with the relative governing equations and boundary conditions. The optimization process is built in TOffee, the house code based on the adjoint optimization method presented in Pietropaoli et al (2017).…”

Density based topology optimization of turbulent flow heat transfer systems

“…The extension to natural convection is described in [4]. Regarding turbulent flow optimization of heat transfer systems, [29] utilizes the frozen turbulence assumption in the calculated sensitivities, and [22] presents continuous adjoint formulation. The present article demonstrates turbulent-flow heat-transfer topology optimization problems where the fluid flow is modeled by steady state incompressible Reynolds-averaged Navier-Stokes (RANS) equations.…”

A design framework for additive manufacturing

“…6 (a) Overhanging angles in a test part manufactured with SLM technology, modified from [18]. (b) Overhanging angle-surface roughness, adapted from [22] flexibility allowing the creation of parts with internal concave channels-achieving great heat convention capabilities or optimum fluid flow [24,25]-or structural reinforcementlattice structures [26,27]. The major AM advantage of conformal cooling channels that follow complex paths through the volume of the part increases the heat transfer capacity which in mold manufacturing processes provides efficient cooling to mold cavities and reduces cooling time [24].…”